首页|工业过程关键指标预测的知识协同进化增强图卷积网络方法

工业过程关键指标预测的知识协同进化增强图卷积网络方法

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  • 国家科技期刊平台
  • NETL
  • NSTL
  • 万方数据
在流程工业关键变量预测领域，已有研究致力于将过程知识与大数据相结合，以实现更高的准确性，降低过拟合风险和提高可解释性。然而，现有工作存在准确的先验知识构建成本高、无法从丰富的数据中挖掘知识等问题，限制了这些方法在实际工业过程中的广泛应用。为了解决这些挑战，本文提出了一种基于知识协同进化的增强图卷积网络方法。首先，利用易获取的过程流图构建低成本的粗粒度流程知识。然后，在图卷积神经网络模型训练中引入图探索，实现知识更新。最后，为了降低知识复杂度并保持一致性，设计了一种知识过滤机制。所提出的方法在基准的脱丁烷塔工艺过程上进行了验证。实验结果表明，该方法具有出色的预测准确性，并获得高质量的新知识。
Enhancing graph convolutional network of knowledge-based co-evolution for industrial process key variable prediction
Efforts have been made in the field of industrial process key variable prediction to integrate process knowl-edge with big data in order to achieve higher accuracy,reduce the risk of overfitting,and improve interpretability.However,existing approaches face challenges such as the high cost of constructing accurate prior knowledge and the inability to extract knowledge from abundant data,which limits their applicability to real industrial processes.To address these issues,this study proposes an enhanced graph convolutional network of knowledge-based co-evolution(KBCE-GCN)method for industrial process key variable prediction.Initially,a coarse-grained process knowledge is constructed from an easily ac-cessible process flow diagram,requiring minimal construction cost.Subsequently,graph exploration is introduced in GCN model training to update the knowledge.Finally,a knowledge filtering mechanism is designed to reduce the complexity of the knowledge and maintain consistency.The proposed KBCE-GCN method is validated using a benchmark debutanizer column process.The experimental results demonstrate excellent prediction accuracy and the acquisition of high-quality new knowledge.

key variable predictionprocess industryknowledge mininggraph convolutional networkdata-knowledge driven modelingdebutanizer column process

牟天昊、邹媛媛、李少远

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上海交通大学自动化系,上海 200240

系统控制与信息处理教育部重点实验室,上海 200240

关键指标预测 流程工业 知识挖掘 图卷积神经网络 数据-知识驱动建模 脱丁烷塔

国家重点研发计划国家自然科学基金国家自然科学基金

2018AAA01017016183301262173224

2024

10.7641/CTA.2023.20352

控制理论与应用
华南理工大学 中国科学院数学与系统科学研究院

控制理论与应用

CSTPCD北大核心
影响因子：1.076
ISSN：1000-8152
年,卷(期)：2024.41(3)
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